Open-Weight LLM · Private & Custom AI
Qwen3.5-27B-DFlash
A lightweight speculative-decoding draft model designed to accelerate Qwen3.5-27B inference in production serving—not standalone, but a throughput multiplier for ops-heavy workloads when paired with a target model.
Qwen3.5-27B-DFlash is a block-diffusion draft model (2.1B parameters) trained to propose multiple tokens in parallel, which a target model then verifies. In private deployments, it delivers 3–6× throughput gains on Nvidia GPUs while preserving output quality. For ops teams running Qwen3.5-27B internally, this model cuts latency and infrastructure cost without changing application logic.
Model facts
Private deployment
Run Qwen3.5-27B-DFlash in your own environment
Deploy via SGLang (recommended, full DFlash support) or vLLM (PR pending). Runs on a single GPU (e.g., H100, B200); architecture keeps all inference in your environment—tokens never leave your infrastructure. Requires custom_code trust and recent inference-engine builds. Setup: specify draft-model path, block size (8 for concurrency, 16 for single-user throughput), and attention backends (FA4 draft, TRT-LLM target). Data stays on-premise; inference latency and throughput are deterministic and measurable.
Operational AI use cases
High-concurrency customer-support automation
Route support tickets to an in-house AI agent powered by Qwen3.5-27B + DFlash. At concurrency 32, DFlash achieves ~2.7–3× speedup, allowing a single GPU to serve 30+ simultaneous agent threads. Reduce support-response time by 60%; keep ticket data and agent reasoning private.
Internal document retrieval and Q&A
Index proprietary docs (contracts, procedures, product specs) and run retrieval-augmented generation (RAG) on Qwen3.5-27B with DFlash as the draft model. Concurrency 1 gains 4–6× speedup. Teams query internal knowledge without uploading to third-party APIs; compliance and confidentiality guaranteed by architecture.
Finance & operations batch processing
Automate expense categorization, invoice parsing, and report summarization with DFlash-accelerated inference. Process thousands of documents daily on private hardware; reduce inference cost-per-token by 50–70% vs. autoregressive baseline. Output stays within your VPC.
Custom AI
As a base for custom AI
DFlash is a productivity layer, not a foundation model. Use Qwen3.5-27B as your base for custom fine-tuning (instruction-following, domain language, proprietary reasoning), then deploy DFlash alongside in production. Ideal if you're building a custom AI product (internal agent, RAG system, or reasoning workflow) and need to cut serving costs while keeping infra private. Not suitable as a standalone model for new custom training.
In the operating system
Where it fits
In an AI OS, DFlash sits at the **inference-optimization** layer: it accelerates the core language model (Qwen3.5-27B) in your agent/workflow execution loop. Sits between your knowledge-base retrieval layer and your business-logic orchestration (agents, tools, memory). Does not replace reasoning, grounding, or orchestration—enhances speed of the model backbone.
Data control & security
Self-hosting DFlash + Qwen3.5-27B means inference happens entirely on your hardware; tokens, hidden states, and outputs never transit external APIs. This is an **architectural control**, not a claim about the model itself being 'secure'—security depends on your network, access controls, and GPU environment. No telemetry, no model-improvement logging, no third-party model updates. Compliance (HIPAA, SOX, GDPR data residency) becomes a deployment choice, not a service-level promise.
Hardware footprint
**Estimate** (unverified; verify with your hardware/batch setup): - Qwen3.5-27B target model: ~56 GB (bfloat16 on H100/B200) - DFlash draft model (2.1B): ~4.5 GB (bfloat16) - Total active VRAM for paired inference: ~60–65 GB on high-concurrency setups; single-user ~55 GB. - Recommended: 1× H100 (80GB) or 1× B200 (192GB). Multi-GPU tensor parallelism available but not required for draft-model use.
Integration
Wire DFlash-accelerated inference into your ops stack via OpenAI-compatible APIs (SGLang exposes `/v1/completions` and `/v1/chat/completions`). Connect to existing agents/orchestrators (LangChain, AutoGPT, custom Python) by treating the inference server as a drop-in replacement for your Qwen3.5-27B endpoint. Requires: (1) SGLang or vLLM instance running with DFlash and Qwen3.5-27B; (2) client library pointing to `http://localhost:30000`; (3) monitoring/logging on your side. No vendor lock-in; inference server runs on your GPU.
When it's not the right fit
- —Your workload is latency-sensitive at concurrency 1 with ultra-short contexts—DFlash overhead (diffusion sampling) may offset gains for single-token completions.
- —You need a standalone model for custom training or fine-tuning. DFlash is inference-only and purpose-built as a draft model; Qwen3.5-27B-base is your training target.
- —Your inference engine (e.g., older vLLM, Ollama, TensorRT) doesn't support DFlash or speculative decoding—fallback to autoregressive baseline or wait for engine updates.
- —Your ops use-case requires sub-millisecond latency (e.g., real-time trading, low-latency fraud detection). DFlash trades single-token latency for bulk throughput.
Alternatives to consider
Llama 3.1-70B with speculative decoding (via vLLM)
Larger base model, broader instruction coverage. Speculative decoding less optimized than DFlash; throughput gains ~1.5–2.5×. Better for open-domain reasoning; more ops-friendly ecosystem.
Mistral 7B with speculative decoding
Lighter footprint (~15 GB bfloat16), faster prefill. Slower absolute throughput than Qwen3.5-27B; speculative gains modest (~1.5–2×). Ideal if you're constrained to <80 GB VRAM.
Grok-2 (xAI) via Hugging Face (if available open-weight)
Comparable 27B scale. Speculative-decoding support unknown; no public DFlash-equivalent draft model. Requires verification of deployment terms and inference-server support.
FAQ
Can I run DFlash on my own servers without internet?
Yes. Download the model weights once from Hugging Face; then run SGLang or vLLM entirely air-gapped on your GPU. No periodic model updates, no telemetry, no cloud dependency. Tokens and outputs never leave your network.
Is DFlash free for commercial use?
Yes. Apache 2.0 license permits commercial deployment, modification, and redistribution without royalties. You must include license attribution in your product/docs. No vendor approval or usage reporting required.
What happens if I use DFlash at concurrency 32 vs. concurrency 1?
At concurrency 32, DFlash delivers ~2.7–3× speedup (tokens/sec) vs. baseline. At concurrency 1, gains are higher (4–6×) because the draft model's cost is amortized over longer generation sequences. Choose block size: 8 for high concurrency, 16 for single-user or longer contexts.
Do I need to fine-tune or retrain DFlash for my domain?
No. DFlash is a fixed draft model paired with Qwen3.5-27B. If you need domain-specific reasoning, fine-tune Qwen3.5-27B itself, then deploy it with the same DFlash draft model. DFlash's draft proposals adapt automatically.
Run Your Own Accelerated LLM. No Cloud Limits.
Build custom AI agents, RAG systems, and ops workflows on Qwen3.5-27B + DFlash, entirely on your hardware. LLM.co helps you architect private inference, integrate with your ops stack, and measure ROI. Start a private deployment today.